Posts Tagged ‘ stats ’

R, academia and the democratization of statistics

December 12, 2011
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R, academia and the democratization of statistics

I am not a statistician but I use statistics, teach some statistics and write about applications of statistics in biological problems. Last week I was in this biostatistics conference, talking with a Ph.D. student who was surprised about this situation … Continue reading →

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My oh my

December 6, 2011
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My oh my

Noted without comment, visit Biostatistics Ryan Gosling !!! for more gems like the one above.

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On the (statistical) road, workshops and R

December 3, 2011
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On the (statistical) road, workshops and R

Things have been a bit quiet at Quantum Forest during the last ten days. Last Monday (Sunday for most readers) I flew to Australia to attend a couple of one-day workshops; one on spatial analysis (in Sydney) and another one … Continue reading →

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Do we need to deal with ‘big data’ in R?

November 22, 2011
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Do we need to deal with ‘big data’ in R?

David Smith at the Revolutions blog posted a nice presentation on “big data” (oh, how I dislike that term). It is a nice piece of work and the Revolution guys manage to process a large amount of records, starting with … Continue reading →

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Teaching with R: the tools

November 1, 2011
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I bought an Android phone, nothing fancy just my first foray in the smartphone world, which is a big change coming from the dumb phone world(*). Everything is different and I am back at being a newbie; this is what … Continue reading →

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Power Tools for Aspiring Data Journalists: R

October 31, 2011
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Power Tools for Aspiring Data Journalists: R

Picking up on Paul Bradshaw’s post A quick exercise for aspiring data journalists which hints at how you can use Google Spreadsheets to grab – and explore – a mortality dataset highlighted by Ben Goldacre in DIY statistical analysis: experience the thrill of touching real data, I thought I’d describe a quick way of analysing

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Covariance structures

October 26, 2011
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Covariance structures

In most mixed linear model packages (e.g. asreml, lme4, nlme, etc) one needs to specify only the model equation (the bit that looks like y ~ factors...) when fitting simple models. We explicitly say nothing about the covariances that complete … Continue reading →

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Queueing up in R, continued

October 20, 2011
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Queueing up in R, continued

Shown above is a queueing simulation. Each diamond represents a person. The vertical line up is the queue; at the bottom are 5 slots where the people are attended. The size of each diamond is proportional to the log of the time it will take them to be attended. Color is used to tell one

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Maximum likelihood

October 13, 2011
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Maximum likelihood

This post is one of those ‘explain to myself how things work’ documents, which are not necessarily completely correct but are close enough to facilitate understanding. Background Let’s assume that we are working with a fairly simple linear model, where … Continue reading →

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Waiting in line, waiting on R

October 13, 2011
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Waiting in line, waiting on R

I should state right away that I know almost nothing about queuing theory. That’s one of the reasons I wanted to do some queuing simulations. Another reason: when I’m waiting in line at the bank, I tend to do mental calculations for how long it should take me to get served. I look at the

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